Integrative Hybrid Metaheuristic Algorithm for Hyperparameter Optimisation in Pre-Trained Convolutional Neural Network Models (I-HAHO)

Hyperparameter optimisation (HPO) remains a fundamental challenge in deep learning, especially for pre-trained convolutional neural networks (CNNs). While pre-trained models reduce the computational burden of training from scratch, their effectiveness depends heavily on tuning parameters such as learning rate, batch size, dropout, weight decay, and optimizer type. The search space of hyperparameters is large, nonlinear, and highly dataset-dependent, making traditional techniques like grid search, random search, and Bayesian optimisation insufficient. This paper introduces I-HAHO, an Integrative Hybrid Metaheuristic Algorithm that combines Artificial Bee Colony (ABC) for global exploration and Harris Hawks Optimisation (HHO) for local exploitation. A diversity-based phase-switching mechanism dynamically regulates exploration and exploitation, allowing the optimiser to adapt its search behaviour to varying landscape conditions. Experiments on CIFAR-10, CIFAR-100, SVHN, and TinyImageNet with three CNN architectures (VGG16, ResNet50, EfficientNet-B0) demonstrate up to 6.9% accuracy improvements. I-HAHO enhances adaptability, scalability, and robustness for hyperparameter tuning.